Chan Zuckerberg Institute doesn't produce much actual research it's mostly fancy dinners, global travel for congresses and conferences and big opulent parties. They actually got in trouble in the building with the landlord for too many parties, there was a problem drunken individuals peeing in the hallways when they had Justin Bieber and other celebs on site (seriously).
Genuine question, there are a lot of overly ambitious efforts like, even though this seems the most ambitious of them all - but is this all optimistic investment or is there any iota of indication that this is a viable path? I am very skeptical of the ai initiatives in medicine and biology where they want to solve problems that humans cannot yet. I would love to be wrong of course
In, say, civil or aerospace engineering, science is understood well enough to allow your building or airplane to be modelled and tested using computer modelling, CAD software, FEM and CFD algorithms and so on. You can design a house or an aircraft without ever building a single physical model, and it will stand (or fly), 99 times out of 100. It is oversimplification to a degree, but sufficiently close approximation.
No such thing exists in biology, pharmaceutics, biotech and so on. The accuracy of computer models and simulation is not sufficient to produce results with single-digit percent accuracy for any metrics, hence long and complex Phase I-II-III trials. Maybe 1 out of 100 candidate drugs works.
Why? Because we do not have the same level of understanding for biological systems as we do for buildings or aircraft, or software. Amount of information is much larger, complexity is far greater, enzymes and cell signalling network make biochemistry extremely non-linear. This makes the problem space vast. It is practically untapped domain and it can eat any amount of computational power and biologists, data scientists and software devs (manpower-wise).
Any incremental improvements in simulation, modelling and interpretation of biological system behaviour will generate downstream improvements in medicine, pharma, biotech. But general-purpose LLM AIs are not that useful in biology, you need more specialized solutions to improve both accuracy and performance of large number of algorithms that have tremendous computational complexity: computational chemistry, molecular dynamics, genomics->proteomics->interactomics->metabolomics (all of that for just intra-cellular behaviour - tissues, organism and organisms are multiple orders of magnitude harder).
But fundamentally it is a problem of missing software to better model biological systems (AI or non-AI). Once created, such a solution will enable large amount of very big breakthroughs in almost every biology-connected discipline.
> there are a lot of overly ambitious efforts like, even though this seems the most ambitious of them all
Chan Zuckerberg is NOTORIOUS for overly ambitious claims, the Chan Zuckerberg Initiative started in 2015 with the bold statement they would "cure all disease in our lifetime." It's been 11 years. Have they cured 1 disease? Let alone ~all~ disease? No.
When Zuckerberg realized he probably wasn't going to hit this goal they quietly changed it to "within our children's lifetimes."
I used to work in their building and actually saw them change it on the wall and as "within our childrens" 3 years in. Stay posted, probably in 15 years they buy themselves some more time and make it "our children's children's lifetimes."
>Have they cured 1 disease? Let alone ~all~ disease? No.
I mean, curing all disease isn't something where progress is linear. A large portion of the work is done upfront before you see any result. Then when your knowledge base and methodology is sufficiently robust, many disease can then be cured in quick succession. The fact that they have no visible success after 10 years says little about the viability of their goal.
Looking at the history of modern medicine, the cure rate of diseases was not exponential, it was gradual over the course of hundreds of years.
Sure there were a few big jumps- water sanitation and antibiotics come to mind- but if you look at cured cardiovascular diseases, cancers, GI diseases etc., they all started with bad treatments that indeed slowly improved over decades.
If CZI is looking to eliminate all disease in a lifetime (say 100 years) I would expect some progress.
I honestly don't think it matters, so long as they're working toward the same guiding direction they'll achieve the same thing regardless of the arbitrary point in the future they pick to aim toward.
It is nice to know when confronted with new information that they might revise their stance too.
Is this an investment that disease and genetics researchers believe will be valuable?
Or is this primarily tax deductible funds flowing back into the AI industrial complex?
(Honest question! If it’s a truly promising path that’s great)
They already donate a lot of useful money via the Chan Zuckerberg Institute so there’s a good track record at least
Chan Zuckerberg Institute doesn't produce much actual research it's mostly fancy dinners, global travel for congresses and conferences and big opulent parties. They actually got in trouble in the building with the landlord for too many parties, there was a problem drunken individuals peeing in the hallways when they had Justin Bieber and other celebs on site (seriously).
https://chanzuckerberg.com/grants-ventures/grants/
A lot of parties.
It is a valuable initiative, regardless of Zuckerbergs personas.
An attempt to live forever IMO.
Is that an issue?
Just in that it’s not gonna work.
They will probably do a rugpull like what they with their children school funding.
Alternate theory: part of the reason they stopped funding the school is to allocate their philanthropy to projects like this one.
It's also what they did with meta.org (a trademark grab followed by a rugpull): https://en.wikipedia.org/wiki/Meta_(academic_company)
Genuine question, there are a lot of overly ambitious efforts like, even though this seems the most ambitious of them all - but is this all optimistic investment or is there any iota of indication that this is a viable path? I am very skeptical of the ai initiatives in medicine and biology where they want to solve problems that humans cannot yet. I would love to be wrong of course
Sort of long answer.
In, say, civil or aerospace engineering, science is understood well enough to allow your building or airplane to be modelled and tested using computer modelling, CAD software, FEM and CFD algorithms and so on. You can design a house or an aircraft without ever building a single physical model, and it will stand (or fly), 99 times out of 100. It is oversimplification to a degree, but sufficiently close approximation.
No such thing exists in biology, pharmaceutics, biotech and so on. The accuracy of computer models and simulation is not sufficient to produce results with single-digit percent accuracy for any metrics, hence long and complex Phase I-II-III trials. Maybe 1 out of 100 candidate drugs works.
Why? Because we do not have the same level of understanding for biological systems as we do for buildings or aircraft, or software. Amount of information is much larger, complexity is far greater, enzymes and cell signalling network make biochemistry extremely non-linear. This makes the problem space vast. It is practically untapped domain and it can eat any amount of computational power and biologists, data scientists and software devs (manpower-wise).
Any incremental improvements in simulation, modelling and interpretation of biological system behaviour will generate downstream improvements in medicine, pharma, biotech. But general-purpose LLM AIs are not that useful in biology, you need more specialized solutions to improve both accuracy and performance of large number of algorithms that have tremendous computational complexity: computational chemistry, molecular dynamics, genomics->proteomics->interactomics->metabolomics (all of that for just intra-cellular behaviour - tissues, organism and organisms are multiple orders of magnitude harder).
But fundamentally it is a problem of missing software to better model biological systems (AI or non-AI). Once created, such a solution will enable large amount of very big breakthroughs in almost every biology-connected discipline.
> there are a lot of overly ambitious efforts like, even though this seems the most ambitious of them all
Chan Zuckerberg is NOTORIOUS for overly ambitious claims, the Chan Zuckerberg Initiative started in 2015 with the bold statement they would "cure all disease in our lifetime." It's been 11 years. Have they cured 1 disease? Let alone ~all~ disease? No.
When Zuckerberg realized he probably wasn't going to hit this goal they quietly changed it to "within our children's lifetimes."
I used to work in their building and actually saw them change it on the wall and as "within our childrens" 3 years in. Stay posted, probably in 15 years they buy themselves some more time and make it "our children's children's lifetimes."
>Have they cured 1 disease? Let alone ~all~ disease? No.
I mean, curing all disease isn't something where progress is linear. A large portion of the work is done upfront before you see any result. Then when your knowledge base and methodology is sufficiently robust, many disease can then be cured in quick succession. The fact that they have no visible success after 10 years says little about the viability of their goal.
Not sure if that’s true.
Looking at the history of modern medicine, the cure rate of diseases was not exponential, it was gradual over the course of hundreds of years.
Sure there were a few big jumps- water sanitation and antibiotics come to mind- but if you look at cured cardiovascular diseases, cancers, GI diseases etc., they all started with bad treatments that indeed slowly improved over decades.
If CZI is looking to eliminate all disease in a lifetime (say 100 years) I would expect some progress.
I honestly don't think it matters, so long as they're working toward the same guiding direction they'll achieve the same thing regardless of the arbitrary point in the future they pick to aim toward.
It is nice to know when confronted with new information that they might revise their stance too.
Worked for AlphaFold, ostensibly.